Overview

Dataset statistics

Number of variables20
Number of observations29500
Missing cells72214
Missing cells (%)12.2%
Duplicate rows107
Duplicate rows (%)0.4%
Total size in memory4.5 MiB
Average record size in memory160.0 B

Variable types

Categorical4
Numeric14
Boolean2

Warnings

Dataset has 107 (0.4%) duplicate rowsDuplicates
weekid has a high cardinality: 3262 distinct values High cardinality
song has a high cardinality: 24359 distinct values High cardinality
performer has a high cardinality: 10059 distinct values High cardinality
week_position is highly correlated with peak_positionHigh correlation
weeks_on_chart is highly correlated with peak_positionHigh correlation
peak_position is highly correlated with week_position and 1 other fieldsHigh correlation
energy is highly correlated with loudness and 1 other fieldsHigh correlation
loudness is highly correlated with energyHigh correlation
acousticness is highly correlated with energyHigh correlation
weeks_on_chart is highly correlated with peak_positionHigh correlation
peak_position is highly correlated with weeks_on_chartHigh correlation
energy is highly correlated with loudness and 1 other fieldsHigh correlation
loudness is highly correlated with energyHigh correlation
acousticness is highly correlated with energyHigh correlation
weeks_on_chart is highly correlated with peak_positionHigh correlation
peak_position is highly correlated with weeks_on_chartHigh correlation
weeks_on_chart is highly correlated with peak_positionHigh correlation
peak_position is highly correlated with weeks_on_chart and 1 other fieldsHigh correlation
acousticness is highly correlated with energyHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
speechiness is highly correlated with spotify_track_explicitHigh correlation
time_signature is highly correlated with tempo and 1 other fieldsHigh correlation
spotify_track_explicit is highly correlated with speechinessHigh correlation
tempo is highly correlated with time_signature and 1 other fieldsHigh correlation
week_position is highly correlated with peak_positionHigh correlation
loudness is highly correlated with energyHigh correlation
danceability is highly correlated with time_signature and 1 other fieldsHigh correlation
spotify_track_duration_ms has 5105 (17.3%) missing values Missing
spotify_track_explicit has 5105 (17.3%) missing values Missing
danceability has 5167 (17.5%) missing values Missing
energy has 5167 (17.5%) missing values Missing
key has 5167 (17.5%) missing values Missing
loudness has 5167 (17.5%) missing values Missing
mode has 5167 (17.5%) missing values Missing
speechiness has 5167 (17.5%) missing values Missing
acousticness has 5167 (17.5%) missing values Missing
instrumentalness has 5167 (17.5%) missing values Missing
liveness has 5167 (17.5%) missing values Missing
valence has 5167 (17.5%) missing values Missing
tempo has 5167 (17.5%) missing values Missing
time_signature has 5167 (17.5%) missing values Missing
song is uniformly distributed Uniform
key has 3035 (10.3%) zeros Zeros
instrumentalness has 10079 (34.2%) zeros Zeros

Reproduction

Analysis started2021-08-15 21:12:17.214179
Analysis finished2021-08-15 21:12:48.032212
Duration30.82 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

weekid
Categorical

HIGH CARDINALITY

Distinct3262
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size230.6 KiB
2021-05-29
 
99
1998-11-28
 
54
2021-01-02
 
38
2019-08-24
 
29
2021-01-09
 
27
Other values (3257)
29253 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters295000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row2009-12-26
2nd row2014-01-04
3rd row2017-07-29
4th row2018-12-22
5th row2019-06-01

Common Values

ValueCountFrequency (%)
2021-05-2999
 
0.3%
1998-11-2854
 
0.2%
2021-01-0238
 
0.1%
2019-08-2429
 
0.1%
2021-01-0927
 
0.1%
2020-03-0724
 
0.1%
1968-08-2423
 
0.1%
2020-03-1423
 
0.1%
1966-03-1223
 
0.1%
2021-05-2223
 
0.1%
Other values (3252)29137
98.8%

Length

2021-08-15T15:12:48.287427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-05-2999
 
0.3%
1998-11-2854
 
0.2%
2021-01-0238
 
0.1%
2019-08-2429
 
0.1%
2021-01-0927
 
0.1%
2020-03-0724
 
0.1%
1968-08-2423
 
0.1%
2020-03-1423
 
0.1%
1966-03-1223
 
0.1%
2021-05-2223
 
0.1%
Other values (3252)29137
98.8%

Most occurring characters

ValueCountFrequency (%)
-59000
20.0%
153501
18.1%
052227
17.7%
932728
11.1%
230028
10.2%
614937
 
5.1%
713289
 
4.5%
812638
 
4.3%
39582
 
3.2%
59098
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number236000
80.0%
Dash Punctuation59000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
153501
22.7%
052227
22.1%
932728
13.9%
230028
12.7%
614937
 
6.3%
713289
 
5.6%
812638
 
5.4%
39582
 
4.1%
59098
 
3.9%
47972
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
-59000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common295000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-59000
20.0%
153501
18.1%
052227
17.7%
932728
11.1%
230028
10.2%
614937
 
5.1%
713289
 
4.5%
812638
 
4.3%
39582
 
3.2%
59098
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII295000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-59000
20.0%
153501
18.1%
052227
17.7%
932728
11.1%
230028
10.2%
614937
 
5.1%
713289
 
4.5%
812638
 
4.3%
39582
 
3.2%
59098
 
3.1%

week_position
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct100
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.5679322
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:48.396665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q159
median85
Q395
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.71969083
Coefficient of variation (CV)0.2836656314
Kurtosis-0.7435583955
Mean76.5679322
Median Absolute Deviation (MAD)13
Skewness-0.7243040582
Sum2258754
Variance471.7449698
MonotonicityNot monotonic
2021-08-15T15:12:48.519637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001715
 
5.8%
991538
 
5.2%
981418
 
4.8%
971387
 
4.7%
961187
 
4.0%
941081
 
3.7%
951054
 
3.6%
93938
 
3.2%
92851
 
2.9%
91826
 
2.8%
Other values (90)17505
59.3%
ValueCountFrequency (%)
11
 
< 0.1%
21
 
< 0.1%
32
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
96
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
1001715
5.8%
991538
5.2%
981418
4.8%
971387
4.7%
961187
4.0%
951054
3.6%
941081
3.7%
93938
3.2%
92851
2.9%
91826
2.8%

song
Categorical

HIGH CARDINALITY
UNIFORM

Distinct24359
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Memory size230.6 KiB
Hold On
 
17
Forever
 
14
Stay
 
14
You
 
14
Angel
 
14
Other values (24354)
29427 

Length

Max length75
Median length15
Mean length16.55481356
Min length1

Characters and Unicode

Total characters488367
Distinct characters96
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21233 ?
Unique (%)72.0%

Sample

1st rowMy Life Would Suck Without You
2nd rowI Have Nothing
3rd rowFamily Feud
4th rowI Don't Let Go
5th rowI Think

Common Values

ValueCountFrequency (%)
Hold On17
 
0.1%
Forever14
 
< 0.1%
Stay14
 
< 0.1%
You14
 
< 0.1%
Angel14
 
< 0.1%
Happy13
 
< 0.1%
Without You12
 
< 0.1%
Smile12
 
< 0.1%
Runaway12
 
< 0.1%
I Need You11
 
< 0.1%
Other values (24349)29367
99.5%

Length

2021-08-15T15:12:48.795463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the3867
 
3.9%
you3088
 
3.1%
i2354
 
2.4%
love2242
 
2.3%
me1963
 
2.0%
a1766
 
1.8%
to1617
 
1.6%
my1387
 
1.4%
of1373
 
1.4%
in1282
 
1.3%
Other values (9564)77824
78.8%

Most occurring characters

ValueCountFrequency (%)
69263
 
14.2%
e45868
 
9.4%
o33707
 
6.9%
n24944
 
5.1%
a23724
 
4.9%
t21130
 
4.3%
i20280
 
4.2%
r19551
 
4.0%
l14592
 
3.0%
h14083
 
2.9%
Other values (86)201225
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter304919
62.4%
Uppercase Letter99278
 
20.3%
Space Separator69263
 
14.2%
Other Punctuation8860
 
1.8%
Open Punctuation2094
 
0.4%
Close Punctuation2092
 
0.4%
Decimal Number1153
 
0.2%
Dash Punctuation673
 
0.1%
Currency Symbol18
 
< 0.1%
Math Symbol15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e45868
15.0%
o33707
11.1%
n24944
 
8.2%
a23724
 
7.8%
t21130
 
6.9%
i20280
 
6.7%
r19551
 
6.4%
l14592
 
4.8%
h14083
 
4.6%
s13367
 
4.4%
Other values (23)73673
24.2%
Uppercase Letter
ValueCountFrequency (%)
T10953
 
11.0%
I7541
 
7.6%
M7223
 
7.3%
S7092
 
7.1%
L6885
 
6.9%
A6264
 
6.3%
W5702
 
5.7%
B5687
 
5.7%
Y4814
 
4.8%
O4522
 
4.6%
Other values (17)32595
32.8%
Other Punctuation
ValueCountFrequency (%)
'5491
62.0%
,913
 
10.3%
.862
 
9.7%
"558
 
6.3%
/275
 
3.1%
!211
 
2.4%
?206
 
2.3%
&170
 
1.9%
*128
 
1.4%
#19
 
0.2%
Other values (5)27
 
0.3%
Decimal Number
ValueCountFrequency (%)
1288
25.0%
2187
16.2%
0152
13.2%
9109
 
9.5%
4105
 
9.1%
379
 
6.9%
574
 
6.4%
758
 
5.0%
656
 
4.9%
845
 
3.9%
Math Symbol
ValueCountFrequency (%)
+12
80.0%
=2
 
13.3%
>1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
(2087
99.7%
[7
 
0.3%
Close Punctuation
ValueCountFrequency (%)
)2085
99.7%
]7
 
0.3%
Space Separator
ValueCountFrequency (%)
69263
100.0%
Dash Punctuation
ValueCountFrequency (%)
-673
100.0%
Currency Symbol
ValueCountFrequency (%)
$18
100.0%
Modifier Symbol
ValueCountFrequency (%)
`2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin404197
82.8%
Common84170
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e45868
 
11.3%
o33707
 
8.3%
n24944
 
6.2%
a23724
 
5.9%
t21130
 
5.2%
i20280
 
5.0%
r19551
 
4.8%
l14592
 
3.6%
h14083
 
3.5%
s13367
 
3.3%
Other values (50)172951
42.8%
Common
ValueCountFrequency (%)
69263
82.3%
'5491
 
6.5%
(2087
 
2.5%
)2085
 
2.5%
,913
 
1.1%
.862
 
1.0%
-673
 
0.8%
"558
 
0.7%
1288
 
0.3%
/275
 
0.3%
Other values (26)1675
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII488350
> 99.9%
Latin 1 Sup17
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69263
 
14.2%
e45868
 
9.4%
o33707
 
6.9%
n24944
 
5.1%
a23724
 
4.9%
t21130
 
4.3%
i20280
 
4.2%
r19551
 
4.0%
l14592
 
3.0%
h14083
 
2.9%
Other values (77)201208
41.2%
Latin 1 Sup
ValueCountFrequency (%)
é5
29.4%
á3
17.6%
ó2
 
11.8%
ñ2
 
11.8%
¿1
 
5.9%
ò1
 
5.9%
à1
 
5.9%
ö1
 
5.9%
Ó1
 
5.9%

performer
Categorical

HIGH CARDINALITY

Distinct10059
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size230.6 KiB
Glee Cast
 
183
Taylor Swift
 
119
Drake
 
99
The Beatles
 
65
Aretha Franklin
 
64
Other values (10054)
28970 

Length

Max length113
Median length12
Mean length15.06430508
Min length1

Characters and Unicode

Total characters444397
Distinct characters81
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6453 ?
Unique (%)21.9%

Sample

1st rowGlee Cast
2nd rowTessanne Chin
3rd rowJAY-Z Featuring Beyonce
4th rowXXXTENTACION
5th rowTyler, The Creator

Common Values

ValueCountFrequency (%)
Glee Cast183
 
0.6%
Taylor Swift119
 
0.4%
Drake99
 
0.3%
The Beatles65
 
0.2%
Aretha Franklin64
 
0.2%
Elton John58
 
0.2%
The Rolling Stones57
 
0.2%
The Beach Boys54
 
0.2%
Stevie Wonder54
 
0.2%
Madonna53
 
0.2%
Other values (10049)28694
97.3%

Length

2021-08-15T15:12:49.083748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the4965
 
6.5%
featuring2540
 
3.3%
2407
 
3.2%
and929
 
1.2%
lil507
 
0.7%
john369
 
0.5%
bobby361
 
0.5%
brown336
 
0.4%
band328
 
0.4%
with305
 
0.4%
Other values (8042)62944
82.8%

Most occurring characters

ValueCountFrequency (%)
46491
 
10.5%
e42641
 
9.6%
a32103
 
7.2%
n28531
 
6.4%
i26042
 
5.9%
r25164
 
5.7%
o22022
 
5.0%
l17723
 
4.0%
t17594
 
4.0%
s16994
 
3.8%
Other values (71)169092
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter313295
70.5%
Uppercase Letter77135
 
17.4%
Space Separator46491
 
10.5%
Other Punctuation5351
 
1.2%
Decimal Number1122
 
0.3%
Dash Punctuation644
 
0.1%
Open Punctuation109
 
< 0.1%
Close Punctuation109
 
< 0.1%
Currency Symbol89
 
< 0.1%
Math Symbol51
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T8442
 
10.9%
B6761
 
8.8%
S5789
 
7.5%
C5452
 
7.1%
F4928
 
6.4%
M4769
 
6.2%
J4633
 
6.0%
D4290
 
5.6%
A3683
 
4.8%
R3568
 
4.6%
Other values (16)24820
32.2%
Lowercase Letter
ValueCountFrequency (%)
e42641
13.6%
a32103
10.2%
n28531
9.1%
i26042
 
8.3%
r25164
 
8.0%
o22022
 
7.0%
l17723
 
5.7%
t17594
 
5.6%
s16994
 
5.4%
h13745
 
4.4%
Other values (16)70736
22.6%
Other Punctuation
ValueCountFrequency (%)
&2360
44.1%
.1603
30.0%
,531
 
9.9%
'476
 
8.9%
"185
 
3.5%
!96
 
1.8%
/75
 
1.4%
?11
 
0.2%
*10
 
0.2%
:4
 
0.1%
Decimal Number
ValueCountFrequency (%)
2243
21.7%
0181
16.1%
5173
15.4%
1162
14.4%
4102
9.1%
379
 
7.0%
667
 
6.0%
956
 
5.0%
731
 
2.8%
828
 
2.5%
Open Punctuation
ValueCountFrequency (%)
(106
97.2%
[3
 
2.8%
Close Punctuation
ValueCountFrequency (%)
)106
97.2%
]3
 
2.8%
Space Separator
ValueCountFrequency (%)
46491
100.0%
Dash Punctuation
ValueCountFrequency (%)
-644
100.0%
Currency Symbol
ValueCountFrequency (%)
$89
100.0%
Math Symbol
ValueCountFrequency (%)
+51
100.0%
Modifier Symbol
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin390430
87.9%
Common53967
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e42641
 
10.9%
a32103
 
8.2%
n28531
 
7.3%
i26042
 
6.7%
r25164
 
6.4%
o22022
 
5.6%
l17723
 
4.5%
t17594
 
4.5%
s16994
 
4.4%
h13745
 
3.5%
Other values (42)147871
37.9%
Common
ValueCountFrequency (%)
46491
86.1%
&2360
 
4.4%
.1603
 
3.0%
-644
 
1.2%
,531
 
1.0%
'476
 
0.9%
2243
 
0.5%
"185
 
0.3%
0181
 
0.3%
5173
 
0.3%
Other values (19)1080
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII444397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46491
 
10.5%
e42641
 
9.6%
a32103
 
7.2%
n28531
 
6.4%
i26042
 
5.9%
r25164
 
5.7%
o22022
 
5.0%
l17723
 
4.0%
t17594
 
4.0%
s16994
 
3.8%
Other values (71)169092
38.0%

weeks_on_chart
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.20318644
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:49.215683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q316
95-th percentile25
Maximum87
Range86
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.337059611
Coefficient of variation (CV)0.7441686037
Kurtosis3.61344583
Mean11.20318644
Median Absolute Deviation (MAD)6
Skewness1.340103597
Sum330494
Variance69.50656295
MonotonicityNot monotonic
2021-08-15T15:12:49.335500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12520
 
8.5%
202354
 
8.0%
21670
 
5.7%
81594
 
5.4%
71580
 
5.4%
61575
 
5.3%
51439
 
4.9%
91413
 
4.8%
31413
 
4.8%
41394
 
4.7%
Other values (59)12548
42.5%
ValueCountFrequency (%)
12520
8.5%
21670
5.7%
31413
4.8%
41394
4.7%
51439
4.9%
61575
5.3%
71580
5.4%
81594
5.4%
91413
4.8%
101279
4.3%
ValueCountFrequency (%)
871
 
< 0.1%
791
 
< 0.1%
762
< 0.1%
691
 
< 0.1%
682
< 0.1%
652
< 0.1%
641
 
< 0.1%
623
< 0.1%
612
< 0.1%
602
< 0.1%

peak_position
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct100
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.40844068
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:49.460602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q119
median47
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)56

Descriptive statistics

Standard deviation30.93113972
Coefficient of variation (CV)0.6524395082
Kurtosis-1.302364969
Mean47.40844068
Median Absolute Deviation (MAD)28
Skewness0.06412696724
Sum1398549
Variance956.7354045
MonotonicityNot monotonic
2021-08-15T15:12:49.590216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11107
 
3.8%
3504
 
1.7%
2499
 
1.7%
4445
 
1.5%
5426
 
1.4%
7408
 
1.4%
6403
 
1.4%
8389
 
1.3%
91384
 
1.3%
10381
 
1.3%
Other values (90)24554
83.2%
ValueCountFrequency (%)
11107
3.8%
2499
1.7%
3504
1.7%
4445
1.5%
5426
 
1.4%
6403
 
1.4%
7408
 
1.4%
8389
 
1.3%
9375
 
1.3%
10381
 
1.3%
ValueCountFrequency (%)
100247
0.8%
99282
1.0%
98302
1.0%
97329
1.1%
96304
1.0%
95302
1.0%
94333
1.1%
93314
1.1%
92353
1.2%
91384
1.3%

spotify_track_duration_ms
Real number (ℝ≥0)

MISSING

Distinct13983
Distinct (%)57.3%
Missing5105
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean220681.1945
Minimum29688
Maximum3079157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:49.962068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum29688
5-th percentile135658.2
Q1175053
median214850
Q3253253
95-th percentile327746.3
Maximum3079157
Range3049469
Interquartile range (IQR)78200

Descriptive statistics

Standard deviation67745.14511
Coefficient of variation (CV)0.3069819576
Kurtosis146.2738291
Mean220681.1945
Median Absolute Deviation (MAD)39150
Skewness4.973578707
Sum5383517741
Variance4589404686
MonotonicityNot monotonic
2021-08-15T15:12:50.084779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20573310
 
< 0.1%
16000010
 
< 0.1%
1960009
 
< 0.1%
2800009
 
< 0.1%
2513339
 
< 0.1%
1615339
 
< 0.1%
2314009
 
< 0.1%
2272669
 
< 0.1%
2294669
 
< 0.1%
1596668
 
< 0.1%
Other values (13973)24304
82.4%
(Missing)5105
 
17.3%
ValueCountFrequency (%)
296881
< 0.1%
370131
< 0.1%
462531
< 0.1%
480001
< 0.1%
492921
< 0.1%
613061
< 0.1%
677491
< 0.1%
690001
< 0.1%
705561
< 0.1%
713061
< 0.1%
ValueCountFrequency (%)
30791571
< 0.1%
15611331
< 0.1%
13670931
< 0.1%
11241531
< 0.1%
10085331
< 0.1%
9921601
< 0.1%
9413601
< 0.1%
9010001
< 0.1%
8693331
< 0.1%
8348661
< 0.1%

spotify_track_explicit
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5105
Missing (%)17.3%
Memory size230.6 KiB
False
21447 
True
2948 
(Missing)
5105 
ValueCountFrequency (%)
False21447
72.7%
True2948
 
10.0%
(Missing)5105
 
17.3%
2021-08-15T15:12:50.172439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

danceability
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct837
Distinct (%)3.4%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.5999513418
Minimum0
Maximum0.988
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:50.253753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.329
Q10.499
median0.608
Q30.708
95-th percentile0.845
Maximum0.988
Range0.988
Interquartile range (IQR)0.209

Descriptive statistics

Standard deviation0.1531327566
Coefficient of variation (CV)0.2552419604
Kurtosis-0.2349437672
Mean0.5999513418
Median Absolute Deviation (MAD)0.104
Skewness-0.2366541587
Sum14598.616
Variance0.02344964115
MonotonicityNot monotonic
2021-08-15T15:12:50.387123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.63786
 
0.3%
0.67482
 
0.3%
0.62882
 
0.3%
0.65281
 
0.3%
0.6680
 
0.3%
0.6179
 
0.3%
0.60279
 
0.3%
0.55177
 
0.3%
0.58976
 
0.3%
0.62374
 
0.3%
Other values (827)23537
79.8%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
01
< 0.1%
0.1031
< 0.1%
0.1051
< 0.1%
0.1071
< 0.1%
0.111
< 0.1%
0.1111
< 0.1%
0.1132
< 0.1%
0.121
< 0.1%
0.1231
< 0.1%
0.1261
< 0.1%
ValueCountFrequency (%)
0.9881
 
< 0.1%
0.9861
 
< 0.1%
0.9811
 
< 0.1%
0.982
 
< 0.1%
0.9782
 
< 0.1%
0.9746
< 0.1%
0.9732
 
< 0.1%
0.9721
 
< 0.1%
0.9711
 
< 0.1%
0.9681
 
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct985
Distinct (%)4.0%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.6181075774
Minimum0.000581
Maximum0.997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:50.518238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.000581
5-th percentile0.2686
Q10.476
median0.634
Q30.778
95-th percentile0.9134
Maximum0.997
Range0.996419
Interquartile range (IQR)0.302

Descriptive statistics

Standard deviation0.1990736472
Coefficient of variation (CV)0.3220695789
Kurtosis-0.5962893818
Mean0.6181075774
Median Absolute Deviation (MAD)0.15
Skewness-0.3293440475
Sum15040.41168
Variance0.039630317
MonotonicityNot monotonic
2021-08-15T15:12:50.641099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7362
 
0.2%
0.67158
 
0.2%
0.72758
 
0.2%
0.7258
 
0.2%
0.74357
 
0.2%
0.80457
 
0.2%
0.64156
 
0.2%
0.6156
 
0.2%
0.62354
 
0.2%
0.70354
 
0.2%
Other values (975)23763
80.6%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
0.0005811
< 0.1%
0.01811
< 0.1%
0.02011
< 0.1%
0.02041
< 0.1%
0.0211
< 0.1%
0.0221
< 0.1%
0.02221
< 0.1%
0.02231
< 0.1%
0.02271
< 0.1%
0.02461
< 0.1%
ValueCountFrequency (%)
0.9971
 
< 0.1%
0.9962
 
< 0.1%
0.9954
< 0.1%
0.9945
< 0.1%
0.9936
< 0.1%
0.9921
 
< 0.1%
0.9917
< 0.1%
0.991
 
< 0.1%
0.9895
< 0.1%
0.9886
< 0.1%

key
Real number (ℝ≥0)

MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean5.231578515
Minimum0
Maximum11
Zeros3035
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:50.744540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.560265626
Coefficient of variation (CV)0.6805337273
Kurtosis-1.29054339
Mean5.231578515
Median Absolute Deviation (MAD)3
Skewness0.00151409637
Sum127300
Variance12.67549133
MonotonicityNot monotonic
2021-08-15T15:12:50.830087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
03035
10.3%
72851
9.7%
22502
8.5%
92448
8.3%
52183
7.4%
12181
7.4%
41885
 
6.4%
101773
 
6.0%
111702
 
5.8%
81519
 
5.1%
Other values (2)2254
7.6%
(Missing)5167
17.5%
ValueCountFrequency (%)
03035
10.3%
12181
7.4%
22502
8.5%
3819
 
2.8%
41885
6.4%
52183
7.4%
61435
4.9%
72851
9.7%
81519
5.1%
92448
8.3%
ValueCountFrequency (%)
111702
5.8%
101773
6.0%
92448
8.3%
81519
5.1%
72851
9.7%
61435
4.9%
52183
7.4%
41885
6.4%
3819
 
2.8%
22502
8.5%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11165
Distinct (%)45.9%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean-8.664383759
Minimum-28.03
Maximum2.291
Zeros0
Zeros (%)0.0%
Negative24331
Negative (%)82.5%
Memory size230.6 KiB
2021-08-15T15:12:50.935396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-28.03
5-th percentile-15.0694
Q1-11.034
median-8.205
Q3-5.856
95-th percentile-3.703
Maximum2.291
Range30.321
Interquartile range (IQR)5.178

Descriptive statistics

Standard deviation3.6010244
Coefficient of variation (CV)-0.4156122928
Kurtosis0.3478938965
Mean-8.664383759
Median Absolute Deviation (MAD)2.538
Skewness-0.6489054219
Sum-210830.45
Variance12.96737673
MonotonicityNot monotonic
2021-08-15T15:12:51.057001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.31810
 
< 0.1%
-9.08810
 
< 0.1%
-7.07610
 
< 0.1%
-5.1810
 
< 0.1%
-5.9879
 
< 0.1%
-8.7499
 
< 0.1%
-5.0769
 
< 0.1%
-5.799
 
< 0.1%
-6.8369
 
< 0.1%
-9.9038
 
< 0.1%
Other values (11155)24240
82.2%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
-28.031
< 0.1%
-27.8611
< 0.1%
-27.1191
< 0.1%
-27.011
< 0.1%
-26.8261
< 0.1%
-26.5491
< 0.1%
-26.1491
< 0.1%
-25.4871
< 0.1%
-25.4731
< 0.1%
-25.0351
< 0.1%
ValueCountFrequency (%)
2.2911
< 0.1%
0.1751
< 0.1%
-0.4631
< 0.1%
-0.5071
< 0.1%
-0.5171
< 0.1%
-0.6981
< 0.1%
-0.811
< 0.1%
-0.8831
< 0.1%
-0.8841
< 0.1%
-0.9451
< 0.1%

mode
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing5167
Missing (%)17.5%
Memory size230.6 KiB
True
17694 
False
6639 
(Missing)
5167 
ValueCountFrequency (%)
True17694
60.0%
False6639
 
22.5%
(Missing)5167
 
17.5%
2021-08-15T15:12:51.142201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

speechiness
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1230
Distinct (%)5.1%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.07355550076
Minimum0
Maximum0.951
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:51.218807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.027
Q10.0321
median0.0413
Q30.0683
95-th percentile0.268
Maximum0.951
Range0.951
Interquartile range (IQR)0.0362

Descriptive statistics

Standard deviation0.08315419853
Coefficient of variation (CV)1.130495988
Kurtosis12.4944506
Mean0.07355550076
Median Absolute Deviation (MAD)0.0116
Skewness3.161316737
Sum1789.826
Variance0.006914620733
MonotonicityNot monotonic
2021-08-15T15:12:51.338881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0295118
 
0.4%
0.0287116
 
0.4%
0.0303115
 
0.4%
0.0299114
 
0.4%
0.0294113
 
0.4%
0.0317112
 
0.4%
0.029111
 
0.4%
0.0306110
 
0.4%
0.0319110
 
0.4%
0.0293109
 
0.4%
Other values (1220)23205
78.7%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
01
 
< 0.1%
0.0221
 
< 0.1%
0.02211
 
< 0.1%
0.02245
< 0.1%
0.02253
< 0.1%
0.02261
 
< 0.1%
0.02272
 
< 0.1%
0.02285
< 0.1%
0.02293
< 0.1%
0.0234
< 0.1%
ValueCountFrequency (%)
0.9511
< 0.1%
0.9241
< 0.1%
0.9131
< 0.1%
0.8941
< 0.1%
0.8881
< 0.1%
0.8581
< 0.1%
0.8551
< 0.1%
0.8471
< 0.1%
0.8462
< 0.1%
0.741
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3057
Distinct (%)12.6%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.2946368962
Minimum2.51 × 10-6
Maximum0.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:51.461935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.51 × 10-6
5-th percentile0.00265
Q10.0467
median0.195
Q30.508
95-th percentile0.839
Maximum0.991
Range0.99099749
Interquartile range (IQR)0.4613

Descriptive statistics

Standard deviation0.2823054681
Coefficient of variation (CV)0.9581470337
Kurtosis-0.7204904632
Mean0.2946368962
Median Absolute Deviation (MAD)0.1748
Skewness0.7615593299
Sum7169.399595
Variance0.07969637731
MonotonicityNot monotonic
2021-08-15T15:12:51.590100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11750
 
0.2%
0.11148
 
0.2%
0.11347
 
0.2%
0.10747
 
0.2%
0.10147
 
0.2%
0.1345
 
0.2%
0.11445
 
0.2%
0.10545
 
0.2%
0.10444
 
0.1%
0.10844
 
0.1%
Other values (3047)23871
80.9%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
2.51 × 10-61
< 0.1%
3.28 × 10-61
< 0.1%
3.29 × 10-61
< 0.1%
5.67 × 10-61
< 0.1%
7.81 × 10-61
< 0.1%
8 × 10-61
< 0.1%
9.4 × 10-61
< 0.1%
9.44 × 10-61
< 0.1%
1.21 × 10-52
< 0.1%
1.24 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9911
 
< 0.1%
0.992
 
< 0.1%
0.9891
 
< 0.1%
0.9884
< 0.1%
0.9875
< 0.1%
0.9861
 
< 0.1%
0.9851
 
< 0.1%
0.9832
 
< 0.1%
0.9822
 
< 0.1%
0.9813
< 0.1%

instrumentalness
Real number (ℝ≥0)

MISSING
ZEROS

Distinct4423
Distinct (%)18.2%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.03254034614
Minimum0
Maximum0.982
Zeros10079
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:51.716827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.53 × 10-6
Q30.00046
95-th percentile0.1694
Maximum0.982
Range0.982
Interquartile range (IQR)0.00046

Descriptive statistics

Standard deviation0.1362787863
Coefficient of variation (CV)4.187994366
Kurtosis25.58618698
Mean0.03254034614
Median Absolute Deviation (MAD)4.53 × 10-6
Skewness5.040325148
Sum791.8042427
Variance0.0185719076
MonotonicityNot monotonic
2021-08-15T15:12:51.838206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010079
34.2%
1.16 × 10-625
 
0.1%
1.03 × 10-525
 
0.1%
1.17 × 10-619
 
0.1%
1.2 × 10-619
 
0.1%
1.19 × 10-618
 
0.1%
1.23 × 10-518
 
0.1%
0.00010418
 
0.1%
1.19 × 10-518
 
0.1%
1.05 × 10-518
 
0.1%
Other values (4413)14076
47.7%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
010079
34.2%
1 × 10-64
 
< 0.1%
1.01 × 10-617
 
0.1%
1.02 × 10-614
 
< 0.1%
1.03 × 10-615
 
0.1%
1.04 × 10-612
 
< 0.1%
1.05 × 10-612
 
< 0.1%
1.06 × 10-66
 
< 0.1%
1.07 × 10-610
 
< 0.1%
1.08 × 10-614
 
< 0.1%
ValueCountFrequency (%)
0.9822
< 0.1%
0.9811
 
< 0.1%
0.9781
 
< 0.1%
0.9662
< 0.1%
0.9652
< 0.1%
0.9633
< 0.1%
0.9622
< 0.1%
0.9612
< 0.1%
0.961
 
< 0.1%
0.9591
 
< 0.1%

liveness
Real number (ℝ≥0)

MISSING

Distinct1611
Distinct (%)6.6%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.1921024152
Minimum0.00967
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:51.961585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.00967
5-th percentile0.0521
Q10.0909
median0.131
Q30.249
95-th percentile0.533
Maximum0.999
Range0.98933
Interquartile range (IQR)0.1581

Descriptive statistics

Standard deviation0.1590751067
Coefficient of variation (CV)0.8280744753
Kurtosis5.183101997
Mean0.1921024152
Median Absolute Deviation (MAD)0.0558
Skewness2.104279463
Sum4674.42807
Variance0.02530488957
MonotonicityNot monotonic
2021-08-15T15:12:52.089278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.106202
 
0.7%
0.111200
 
0.7%
0.107197
 
0.7%
0.108189
 
0.6%
0.11185
 
0.6%
0.105183
 
0.6%
0.114180
 
0.6%
0.109179
 
0.6%
0.112176
 
0.6%
0.104175
 
0.6%
Other values (1601)22467
76.2%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
0.009671
< 0.1%
0.0131
< 0.1%
0.01362
< 0.1%
0.01451
< 0.1%
0.01461
< 0.1%
0.01661
< 0.1%
0.01691
< 0.1%
0.01841
< 0.1%
0.01861
< 0.1%
0.01891
< 0.1%
ValueCountFrequency (%)
0.9991
< 0.1%
0.9971
< 0.1%
0.9911
< 0.1%
0.992
< 0.1%
0.9892
< 0.1%
0.9882
< 0.1%
0.9871
< 0.1%
0.9861
< 0.1%
0.9842
< 0.1%
0.9821
< 0.1%

valence
Real number (ℝ≥0)

MISSING

Distinct1103
Distinct (%)4.5%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.6017609789
Minimum0
Maximum0.991
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:52.215691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.189
Q10.415
median0.622
Q30.802
95-th percentile0.955
Maximum0.991
Range0.991
Interquartile range (IQR)0.387

Descriptive statistics

Standard deviation0.2386377227
Coefficient of variation (CV)0.3965656316
Kurtosis-0.940241214
Mean0.6017609789
Median Absolute Deviation (MAD)0.192
Skewness-0.2711212212
Sum14642.6499
Variance0.05694796268
MonotonicityNot monotonic
2021-08-15T15:12:52.339817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961136
 
0.5%
0.962118
 
0.4%
0.963118
 
0.4%
0.964100
 
0.3%
0.96591
 
0.3%
0.9683
 
0.3%
0.96671
 
0.2%
0.96958
 
0.2%
0.96757
 
0.2%
0.92656
 
0.2%
Other values (1093)23445
79.5%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
01
< 0.1%
0.03491
< 0.1%
0.03521
< 0.1%
0.03591
< 0.1%
0.03611
< 0.1%
0.03631
< 0.1%
0.03641
< 0.1%
0.03712
< 0.1%
0.03731
< 0.1%
0.03761
< 0.1%
ValueCountFrequency (%)
0.9911
 
< 0.1%
0.991
 
< 0.1%
0.9891
 
< 0.1%
0.9881
 
< 0.1%
0.9871
 
< 0.1%
0.9861
 
< 0.1%
0.9853
< 0.1%
0.9841
 
< 0.1%
0.9832
< 0.1%
0.9822
< 0.1%

tempo
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct20484
Distinct (%)84.2%
Missing5167
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean120.2775407
Minimum0
Maximum241.009
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size230.6 KiB
2021-08-15T15:12:52.462188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.1556
Q199.063
median118.913
Q3136.484
95-th percentile173.828
Maximum241.009
Range241.009
Interquartile range (IQR)37.421

Descriptive statistics

Standard deviation28.04656978
Coefficient of variation (CV)0.2331821021
Kurtosis0.01721647799
Mean120.2775407
Median Absolute Deviation (MAD)18.926
Skewness0.5138985749
Sum2926713.398
Variance786.6100765
MonotonicityNot monotonic
2021-08-15T15:12:52.588985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.9878
 
< 0.1%
119.9917
 
< 0.1%
1136
 
< 0.1%
125.0086
 
< 0.1%
120.086
 
< 0.1%
119.9996
 
< 0.1%
99.9916
 
< 0.1%
1166
 
< 0.1%
140.0226
 
< 0.1%
128.0026
 
< 0.1%
Other values (20474)24270
82.3%
(Missing)5167
 
17.5%
ValueCountFrequency (%)
01
< 0.1%
35.7161
< 0.1%
36.711
< 0.1%
37.1141
< 0.1%
47.941
< 0.1%
48.7181
< 0.1%
50.9371
< 0.1%
51.3911
< 0.1%
51.4091
< 0.1%
53.9191
< 0.1%
ValueCountFrequency (%)
241.0091
< 0.1%
233.4291
< 0.1%
216.21
< 0.1%
213.7371
< 0.1%
211.2611
< 0.1%
211.2061
< 0.1%
210.8741
< 0.1%
210.751
< 0.1%
210.1861
< 0.1%
209.8191
< 0.1%

time_signature
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing5167
Missing (%)17.5%
Memory size230.6 KiB
4.0
22489 
3.0
 
1564
5.0
 
186
1.0
 
92
0.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72999
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.022489
76.2%
3.01564
 
5.3%
5.0186
 
0.6%
1.092
 
0.3%
0.02
 
< 0.1%
(Missing)5167
 
17.5%

Length

2021-08-15T15:12:52.855926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-15T15:12:52.918121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4.022489
92.4%
3.01564
 
6.4%
5.0186
 
0.8%
1.092
 
0.4%
0.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
024335
33.3%
.24333
33.3%
422489
30.8%
31564
 
2.1%
5186
 
0.3%
192
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48666
66.7%
Other Punctuation24333
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024335
50.0%
422489
46.2%
31564
 
3.2%
5186
 
0.4%
192
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.24333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common72999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024335
33.3%
.24333
33.3%
422489
30.8%
31564
 
2.1%
5186
 
0.3%
192
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII72999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024335
33.3%
.24333
33.3%
422489
30.8%
31564
 
2.1%
5186
 
0.3%
192
 
0.1%

Interactions

2021-08-15T15:12:23.589694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:23.737010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:23.843503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.084268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.204731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.306292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.409407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.537147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.670746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.780569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:24.897963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.017849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.133782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.257896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.364414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.482339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.603497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.705934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.829279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:25.941945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.046490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.150064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.258695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.357158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.457442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.565655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.745538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.872154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:26.969300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.077975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.198578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.300708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.413081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.628698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.755666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.875588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:27.994011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.116605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.218096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.319594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.420247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.525929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.637570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.759143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.869015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:28.976998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.096793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.220611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.334610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.451861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.561600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.670802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.778696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:29.900126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.023096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.139214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.247088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.350680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.496311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.615959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.724135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.833224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:30.951587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:31.053733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:31.159813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:31.305950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:31.451556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:31.562177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:31.665067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:31.897134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.018915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.120798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.220891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.322514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.459222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.582840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.683278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.780801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.880379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:32.984624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.104203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.224526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.362354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.466089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.578965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.679903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.776712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.880867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:33.991950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.091491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.189689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.286027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.385227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.504991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.626446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.744526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.854404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:34.958100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.058846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.161191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.263187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.369052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.479061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.585472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.719111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.825905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:35.930612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.040873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.168055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.275904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.376104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.475718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.574800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.832530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:36.947312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.045731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.169034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.307475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.431944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.539747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.667476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.783344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.892461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:37.998375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.105367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.218007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.345092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.465879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.580266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.687941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.815689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:38.936636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.062082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.194456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.318369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.419063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.523026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.635259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.777269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:39.894529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.006881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.118198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.347399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.467543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.614670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.731999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.834847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:40.934665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.040706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.153266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.314323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.460406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.573558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.679880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.793083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:41.895152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.003162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.127336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.248741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.365862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.471890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.581506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.686493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.785578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.885136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:42.985009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:43.084567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:43.183007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:43.472845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:43.597808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:43.713380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:43.829191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:43.958519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.071260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.190510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.319293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.439107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.540084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.642496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.743652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.843544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:44.948759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.059617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.159954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.258982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.367174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.481855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.584037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.682781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.795630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:45.909053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:46.021400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:46.131648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:46.239811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:46.347892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T15:12:46.456038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-15T15:12:53.012311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-15T15:12:53.214856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-15T15:12:53.418530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-15T15:12:53.624850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-15T15:12:53.819237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-15T15:12:46.726046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-15T15:12:47.137821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-15T15:12:47.514046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-15T15:12:47.866189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

weekidweek_positionsongperformerweeks_on_chartpeak_positionspotify_track_duration_msspotify_track_explicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
02009-12-2651My Life Would Suck Without YouGlee Cast151211800.0False0.4710.6639.0-6.284True0.03480.017700.0000000.08010.449145.0784.0
12014-01-0451I Have NothingTessanne Chin151231141.0False0.3980.6399.0-3.517False0.03380.235000.0000000.09720.197154.2473.0
22017-07-2951Family FeudJAY-Z Featuring Beyonce151251413.0TrueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32018-12-2251I Don't Let GoXXXTENTACION151NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
42019-06-0151I ThinkTyler, The Creator151212013.0True0.8260.5798.0-8.241False0.08010.008810.0000000.12900.431121.0754.0
52019-09-0751I Think He KnowsTaylor Swift151173386.0False0.8970.3660.0-8.029True0.05690.008890.0003530.07150.416100.0034.0
62020-05-1651LossesDrake151271183.0True0.5510.5701.0-7.385False0.35100.280000.0000000.42400.50585.6874.0
72010-10-1652I'm A Slave 4 UGlee Cast152204053.0False0.9090.7790.0-4.004False0.13600.409000.0006810.03980.955110.0224.0
82011-04-0252MiseryGlee Cast152188800.0False0.7490.6317.0-5.826True0.05690.188000.0000000.11000.812102.9624.0
92011-09-1752MegaManLil Wayne152198280.0True0.5040.85611.0-5.024False0.28600.011400.0000000.25600.335101.8665.0

Last rows

weekidweek_positionsongperformerweeks_on_chartpeak_positionspotify_track_duration_msspotify_track_explicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
294901962-07-2877Sweet And LovelyApril Stevens & Nino Tempo477175533.0False0.6690.51510.0-11.804True0.03960.6170.00.30600.865121.9284.0
294911963-01-2677Darkest Street In TownJimmy Clanton477139760.0False0.5020.8325.0-7.354False0.03940.4170.00.08970.975147.7784.0
294921966-07-2377Ain't Gonna Cry No MoreBrenda Lee477NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
294931977-12-0377Still The Lovin' Is FunB.J. Thomas477NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
294941964-04-1178Roll Over BeethovenThe Beatles468165466.0False0.3510.7492.0-9.435True0.06280.2890.00.09520.967160.6734.0
294951959-03-1478Bunny HopThe Applejacks470NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
294961967-03-1178Peek-A-BooThe New Vaudeville Band472NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
294971964-01-2578Do-Wah-DiddyThe Exciters478NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
294981965-08-2878Summer WindWayne Newton478157760.0False0.4690.3276.0-15.184True0.03430.4320.00.26900.556114.6474.0
294991968-10-1978Oh Lord, Why LordLos Pop Tops478NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

weekidweek_positionsongperformerweeks_on_chartpeak_positionspotify_track_duration_msspotify_track_explicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature# duplicates
02019-06-2945When The Party's OverBillie Eilish3229196077.0False0.3670.1114.0-14.084True0.09720.978000.0000400.08970.19882.6424.02
12019-06-2947Envy MeCalboy2731120133.0True0.7400.4881.0-7.664False0.27000.234000.0000000.24100.584149.0424.02
22019-06-2970Miss Me MoreKelsea Ballerini2047192840.0False0.6430.7202.0-7.146True0.05270.014000.0000000.08340.49196.0284.02
32019-06-2999Racks In The MiddleNipsey Hussle Featuring Roddy Ricch & Hit-Boy1199233277.0True0.6710.8335.0-5.152False0.39500.082500.0000000.07260.70279.3514.02
42019-07-0687Here With MeMarshmello Featuring CHVRCHES1631156346.0False0.7910.5655.0-3.933False0.04390.062300.0000000.15600.18199.9614.02
52019-07-0688Love SomeoneBrett Eldredge1052211720.0False0.4230.8139.0-5.011True0.05070.218000.0000020.07320.421173.9954.02
62019-07-0694Big Ole FreakMegan Thee Stallion1265214850.0True0.7990.6992.0-7.694True0.20400.008670.0000000.13200.627142.9794.02
72019-07-0696You StayDJ Khaled Featuring Meek Mill, J Balvin, Lil Baby & Jeremih496275275.0True0.4790.6307.0-5.628False0.13300.031400.0000000.07300.227117.9885.02
82019-07-1371HomicideLogic Featuring Eminem671245386.0True0.6940.75910.0-5.667False0.39800.137000.0000000.16700.770140.0554.02
92019-07-1388Eyes On YouChase Rice2038182493.0False0.6060.6529.0-6.982True0.02810.323000.0000000.20100.47697.1554.02